Hasil untuk "Diseases of the digestive system. Gastroenterology"

L Cif, D Demailly, JP Lin et al.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012).

Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid et al.

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.

Nathalia A. Loureiro, Camilo R. Neto, Jack Sutton et al.

Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters.

Ke Feng, Martin Haenggi

Physical contact or proximity is often a necessary condition for the spread of infectious diseases. Common destinations, typically referred to as hubs or points of interest, are arguably the most effective spots for the type of disease spread via airborne transmission. In this work, we model the locations of individuals (agents) and common destinations (hubs) by random spatial point processes in $\mathbb{R}^d$ and focus on disease propagation through agents visiting common hubs. The probability of an agent visiting a hub depends on their distance through a connection function $f$. The system is represented by a random bipartite geometric (RBG) graph. We study the degrees and percolation of the RBG graph for general connection functions. We show that the critical density of hubs for percolation is dictated by the support of the connection function $f$, which reveals the critical role of long-distance travel (or its restrictions) in disease spreading.

Xuanzhong Chen, Xiaohao Mao, Qihan Guo et al.

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.

Nikita Kuzmin, Hieu-Thi Luong, Jixun Yao et al.

In this work, we describe our submissions for the Voice Privacy Challenge 2024. Rather than proposing a novel speech anonymization system, we enhance the provided baselines to meet all required conditions and improve evaluated metrics. Specifically, we implement emotion embedding and experiment with WavLM and ECAPA2 speaker embedders for the B3 baseline. Additionally, we compare different speaker and prosody anonymization techniques. Furthermore, we introduce Mean Reversion F0 for B5, which helps to enhance privacy without a loss in utility. Finally, we explore disentanglement models, namely $β$-VAE and NaturalSpeech3 FACodec.

A. A. Sheptulin, Y. S. Rabotyagova

Aim: to present the main statements of Kyoto International Consensus report on anatomy, pathophysiology, and clinical significance of the gastroesophageal junction.Key points. The experts reviewed and adopted 28 statements concerning (1) the definition of the gastroesophageal junction (GEJ); (2) the definition of the GEJ zone, covering the area located 1 cm proximal and 1 cm distal in relation to gastroesophageal junction; (3) the assessment of chemical and bacterial (Helicobacter pylori) factors leading to the development of inflammation, metaplasia and neoplasia of the mucosa of the GEJ; and (4) a new definition of Barrett’s esophagus.Conclusion. The new definitions of GEJ, GEJ zone and Barrett’s esophagus adopted by the International Consensus will be used in subsequent studies, which will contribute to improving the results of treatment of diseases of this area.

Yee Hui Yeo, Jamil S. Samaan, Wee Han Ng

Ricardo Guilherme VIEBIG, Fernando PARDINI

Peilin Yu, Stephen H. Bach

Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models. Alfred provides a simple Python interface for the key steps of this emerging paradigm, with a high-throughput backend for large-scale data labeling. Users can quickly create, evaluate, and refine their prompt-based weak supervision sources; map the results to weak labels; and resolve their disagreements with a label model. Alfred enables a seamless local development experience backed by models served from self-managed computing clusters. It automatically optimizes the execution of prompts with optimized batching mechanisms. We find that this optimization improves query throughput by 2.9x versus a naive approach. We present two example use cases demonstrating Alfred on YouTube comment spam detection and pet breeds classification. Alfred is open source, available at https://github.com/BatsResearch/alfred.

Yousuf Rayhan Emon, Md Golam Rabbani, Md. Taimur Ahad et al.

Sweet orange leaf diseases are significant to agricultural productivity. Leaf diseases impact fruit quality in the citrus industry. The apparition of machine learning makes the development of disease finder. Early detection and diagnosis are necessary for leaf management. Sweet orange leaf disease-predicting automated systems have already been developed using different image-processing techniques. This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification. The benefits and limitations of different machine learning models, including Vision Transformer (ViT), Neural Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP, EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine learning models tested on various datasets and detected the disease. This comprehensive review study related to leaf disease compares the performance of the models; those models' accuracy, precision, recall, etc., were used in the subsisting studies

Ahmad AlEid, Areej Al Balkhi, Adel Qutub et al.

Background: Cost, adverse events, and long treatment duration can be significant obstacles in treating hepatitis C virus (HCV)-infected individuals. Shortening the treatment regimen can minimize these barriers, thereby enhancing adherence and increasing medication availability to more patients. Methods: This is a single-centre, single-arm, open-label, phase 3 clinical trial on treatment naïve, non-cirrhotic, HCV genotype 4 patients. The study aimed to evaluate an 8-week course of Elbasvir (ELB)/Grazoprevir (GZR) in this population. The primary endpoint was sustained virologic response at 12 weeks after the end of treatment (SVR-12). The secondary endpoints were SVR-4, adverse events, and changes in health- and hepatitis-related quality of life (HRQoL). Results: Of the 30 patients who were enrolled, 29 (97%) achieved SVR-12 and SVR-4 (95% CI: 90-100%). No patients experienced serious or life-threatening adverse events (AEs), but mild/moderate AEs were reported by 16 (53%). The most commonly reported AEs were itching/skin rash (20%), headache (16.7%), abdominal/epigastric pain and decreased appetite (13.3% each), and nausea/vomiting (10%). Marked improvements in HRQoL were reported between the first (baseline) and third (SVR-12) timepoints. HRQoL score improvements involved the physical, mental, and hepatitis-specific indices, and ranged between 6 and 42 points (out of 100, P ≤0.003). Conclusion: The trial provides empirical evidence that HCV genotype 4-infected patients can achieve viral eradication with an 8-week-regimen of ELB/GZR. Further, this course of treatment is associated with a minimal adverse event profile and potentially significant improvements in quality of life. (ClinicalTrials.gov number, NCT03578640).

Adan Aftab, Nida Saleem, Syed Farhat Abbas et al.

Background. Although lupus enteritis is a rare manifestation of systemic lupus erythematosus yet results in significant distress. This disorder contributes to diagnostic and therapeutic dilemma leading to enhanced mortality. Case Description. We report a case history of a 29-year-old female who presented with severe abdominal pain, watery stools, and vomiting, and later on, she developed pancytopenia and renal impairment. On intensive workup, diagnosis of lupus-associated enteritis, nephritis, and pancytopenia was discovered. She improved drastically on initiation of plasmapheresis followed by low-dose intravenous rituximab. One year posttreatment, she remained in complete remission. Conclusion. From this case, it can be suggested that in a young female with intractable abdominal pain, the remote possibility of lupus enteritis must be kept in mind. Besides this, plasmapheresis can have a potential role in refractory lupus enteritis. Furthermore, low-dose intravenous rituximab can be a safe and cost-effective treatment option in achieving sustained remission of clinical and laboratory parameters in lupus enteritis.

Chi‐Tung Cheng, Jinzheng Cai, Wei Teng et al.

Abstract Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios (e.g., fully dynamic contrast‐enhanced [DCE] studies, noncontrast [NC] plus venous phase [VP] abdominal studies, or NC‐only studies). Each scenario presents its own clinical challenges that could benefit from computer‐aided detection (CADe) tools. We investigate whether a single CADe model can be made flexible enough to handle different contrast protocols and whether this flexibility imparts performance gains. We developed a flexible three‐dimensional deep algorithm, called heterophase volumetric detection (HPVD), that can accept any combination of contrast‐phase inputs with adjustable sensitivity depending on the clinical purpose. We trained HPVD on 771 DCE CT scans to detect HCCs and evaluated it on 164 positives and 206 controls. We compared performance against six clinical readers, including two radiologists, two hepatopancreaticobiliary surgeons, and two hepatologists. The area under the curve of the localization receiver operating characteristic for NC‐only, NC plus VP, and full DCE CT yielded 0.71 (95% confidence interval [CI], 0.64–0.77), 0.81 (95% CI, 0.75–0.87), and 0.89 (95% CI, 0.84–0.93), respectively. At a high‐sensitivity operating point of 80% on DCE CT, HPVD achieved 97% specificity, which is comparable to measured physician performance. We also demonstrated performance improvements over more typical and less flexible nonheterophase detectors. Conclusion: A single deep‐learning algorithm can be effectively applied to diverse HCC detection clinical scenarios, indicating that HPVD could serve as a useful clinical aid for at‐risk and opportunistic HCC surveillance.

Petchiammal A, Briskline Kiruba S, D. Murugan et al.

One of the critical biotic stress factors paddy farmers face is diseases caused by bacteria, fungi, and other organisms. These diseases affect plants' health severely and lead to significant crop loss. Most of these diseases can be identified by regularly observing the leaves and stems under expert supervision. In a country with vast agricultural regions and limited crop protection experts, manual identification of paddy diseases is challenging. Thus, to add a solution to this problem, it is necessary to automate the disease identification process and provide easily accessible decision support tools to enable effective crop protection measures. However, the lack of availability of public datasets with detailed disease information limits the practical implementation of accurate disease detection systems. This paper presents \emph{Paddy Doctor}, a visual image dataset for identifying paddy diseases. Our dataset contains 16,225 annotated paddy leaf images across 13 classes (12 diseases and normal leaf). We benchmarked the \emph{Paddy Doctor} dataset using a Convolutional Neural Network (CNN) and four transfer learning based models (VGG16, MobileNet, Xception, and ResNet34). The experimental results showed that ResNet34 achieved the highest F1-score of 97.50%. We release our dataset and reproducible code in the open source for community use.

Magdalena K. Stoeva, Jeewon Garcia-So, Nicholas Justice et al.

Clostridium butyricum is a butyrate-producing human gut symbiont that has been safely used as a probiotic for decades. C. butyricum strains have been investigated for potential protective or ameliorative effects in a wide range of human diseases, including gut-acquired infection, intestinal injury, irritable bowel syndrome, inflammatory bowel disease, neurodegenerative disease, metabolic disease, and colorectal cancer. In this review we summarize the studies on C. butyricum supplementation with special attention to proposed mechanisms for the associated health benefits and the supporting experimental evidence. These mechanisms center on molecular signals (especially butyrate) as well as immunological signals in the digestive system that cascade well beyond the gut to the liver, adipose tissue, brain, and more. The safety of probiotic C. butyricum strains appears well-established. We identify areas where additional human randomized controlled trials would provide valuable further data related to the strains’ utility as an intervention.

Nikolaj W. Ørntoft, Ole L. Munk, Michael Sørensen

Giorgio Grani, Lorenzo Madeddu, Paola Velardi

Objective: Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of observable pathological similarities between disorders, often based on anatomical or histological principles. Although, thanks to recent advances in molecular biology, disease ontologies are slowly changing to integrate the etiological and genetic origins of diseases, nosology still reflects this "reductionist" perspective. Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery. On the other hand, similarity relations induced from structural proximity of DMs also have several limitations, such as incomplete knowledge of disease-gene relationships and reliability of clinical trials to assess their validity. The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related DM in the interactome. Method: We propose a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy. Results: We demonstrate that the proposed methodology allows to systematically analyze commonalities and differences among structural and categorical similarity of human diseases, help refine and extend human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.